Co-located with HiPEAC 2022 (Budapest), January 19, 2022 June 22, 2022
Giorgio Buttazzo is Full Professor of Computer Engineering at the Scuola Superiore Sant'Anna of Pisa. He graduated in Electronic Engineering at the University of Pisa in 1985, received a Master in Computer Science at the University of Pennsylvania in 1987, and a Ph.D. in Computer Engineering at the Scuola Superiore Sant'Anna of Pisa in 1991. From 1987 to 1988, he worked on active perception and real-time control at the G.R.A.S.P. Laboratory of the University of Pennsylvania, Philadelphia. From 1991 to 1998, he held a position of Assistant Professor at the Scuola Superiore Sant'Anna of Pisa, where he founded and directed the RETIS Laboratory, one of the world leading research groups on real-time systems. From 1998 to 2005, he held a position of Associate Professor at the University of Pavia, where he directed the Robotics Laboratory of the Computer Science department. In 2003, he was co-founder of Evidence s.r.l., a spin-off company of the Scuola Superiore Sant'Anna providing software solutions for real-time embedded systems. He has been Chair of the IEEE Technical Committee on Real-Time Systems (2010-2012), and Program Chair and General Chair of the major international conferences on real-time computing. He is IEEE Fellow since 2012 "for contributions to dynamic scheduling algorithms in real-time systems". In 2013, he received the Outstanding Technical Contributions and Leadership Award from the IEEE Technical Committee on Real-Time Systems. He is Editor-in-Chief of the Journal of Real-Time Systems (Springer), the major journal on real-time computing, and has been Associate Editor of the IEEE Transactions on Industrial Informatics and the ACM Transactions on Cyber-Physical Systems. He has authored 6 books on real-time systems and over 300 papers in the field of real-time systems, robotics, and neural networks, receiving 13 Best Paper Awards.
The excellent performance of deep neural networks and machine learning algorithms is pushing the industry to adopt such a technology in several application domains, including safety-critical ones, as self-driving vehicles, autonomous robots, and diagnosis support systems for medical applications. However, most of the AI methodologies available today have not been designed to work in safety10 critical environments and several issues need to be solved, at different architecture levels, to make them trustworthy. This paper presents some of the major problems existing today in AI-powered embedded systems, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.